We use Gaussian QKL combined with one competitive metric learning solution of prototypical networks [1]}, [2]}. Our training objective is jointly built upon generalised end-to-end loss [3]} and angular variant prototypical loss used in [2]} for training with low-resource speech data. For model training, we first collect latent feature embeddings projected from output measurement of quantum kernel and use prototypical loss to maximize class-wise distance in the latent space. We train a prediction backbone with kernel SVM referred to the setup in [2]}. We then re-use the quantum projection with kernel learning in Eq. (REF ) from the NISQ device for kernel SVM learning as a hybrid model. Similar to the findings in [2]}, kernel SVM performs better than DNN serving a backbone prediction model evaluated in our low-resource command data.
